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Python Institute PCAD-31-02 Exam Syllabus Topics:

Section	Weight	Objectives
Python Programming for Data Analysis	30%	- Control Flow and Functions 1. Conditional statements (if, elif, else) 2. Function definitions and parameters 3. Return values and scope 4. Loops (for, while) - Python Data Types and Structures 1. Numbers, strings, booleans 2. Data type conversions 3. Lists, tuples, dictionaries, sets - File Operations 1. Reading from files (text, CSV) 2. Context managers (with statement) 3. Writing to files
Applied Data Analysis Projects	20%	- Exploratory Data Analysis (EDA) 1. Data distribution analysis 2. Correlation analysis 3. Descriptive statistics computation 4. Pattern identification - Data Analysis Workflow 1. Data exploration and cleaning 2. Analysis and modeling 3. Problem definition 4. Results interpretation and presentation
Working with Data Using Python Libraries	30%	- NumPy Fundamentals 1. Vectorized operations 2. Array indexing and slicing 3. NumPy arrays and operations 4. Basic statistical functions - Pandas Library 1. GroupBy operations 2. Handling missing data 3. Series and DataFrame structures 4. Data selection and filtering 5. DataFrame operations (merge, join, concat) - Data Visualization 1. Seaborn introduction 2. Matplotlib basics 3. Creating basic charts (line, bar, scatter, histogram) 4. Customizing plots
Data Analysis Fundamentals	20%	- Introduction to Data Analysis 1. Types of data (structured, unstructured, semi-structured) 2. Data analysis process lifecycle 3. Data analysis concepts and terminology - Data Collection and Preparation 1. Data import/export operations 2. Data sources and acquisition methods 3. Data cleaning and preprocessing basics

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q38-Q43):

NEW QUESTION # 38
Which features of object-oriented patterns improve clarity and reusability in Python analysis workflows?
(Choose all that apply)

A. Applying inheritance to avoid redundant code
B. Encapsulating behavior in class methods
C. Using global variables instead of object properties
D. Replacing all functions with lambda expressions

Answer: A,B

NEW QUESTION # 39
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

A. list comprehensions
B. decorators
C. while loops
D. match-case statements

Answer: A,C

NEW QUESTION # 40
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

A. Use the requests module to retrieve JSON responses
B. Load CSV directly with re.compile()
C. Open Excel files using matplotlib
D. Access data using requests.get() and parse it with .json()

Answer: A,D

NEW QUESTION # 41
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

A. ORDER BY
B. WHERE
C. HAVING
D. GROUP BY

Answer: B

NEW QUESTION # 42
Which packages are most commonly used together in a data science workflow for data manipulation and visualization?
(Choose two)

A. flask
B. pandas
C. NumPy
D. Pygame

Answer: B,C

NEW QUESTION # 43
......

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